cs.ROSep 28, 2026

Graph-Based Simultaneous Path and Foothold Planning for Multi-Limbed Intra-Vehicular Robots in Space Stations

Authors: Masazumi Imai, Kentaro Uno, Toshinori Kuwahara, Kazuya Yoshida

Organizations: Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan · New Industry Creation Hatchery Center (NICHe), Tohoku University, Sendai 980-8579, Japan · Research Center for Green X-Tech and Research Center for Space Cross-Tech, Green Goals Initiative, Tohoku University, Sendai 980-8579, Japan

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Abstract

Robot-aided operations in space stations are essential for reducing the workload of astronauts and improving the efficiency of on-orbit activities. Multi-limbed intra-vehicular robots (MLIVRs) equipped with grappling end-effectors have emerged as a promising solution, as they can securely grasp pre-existing interfaces, such as handrails and seat tracks, thereby enabling stable locomotion and forceful manipulation in microgravity environments. Since graspable locations on these interfaces are spatially limited and discretely distributed, motion planning for MLIVRs must be addressed jointly with foothold planning. This paper presents a simultaneous path and foothold planning framework based on graph theory for MLIVRs. The proposed method efficiently searches for feasible stance sequences for a multi-limbed robot while satisfying manipulability constraints. The effectiveness of the proposed framework is validated through simulations in a 3D model of the International Space Station (ISS) cabin, demonstrating its capability to generate feasible and efficient locomotion plans in realistic intra-vehicular environments.

Figures & tables

Fig. 1: Rail-gripping locomotion of a multi-limbed intra-vehicular robot (MLIVR) in a space-station environment. Grasping locations (footholds) are discretely distributed along rails, such as seat tracks and handrails, inside the station module. The proposed planner simultaneously determines the optimal base trajectory (thick green line) and foothold sequence (red and blue lines) from all feasible locomotion paths (thin green lines) represented in a graph structure. Candidate stances that violate kinematic constraints or exhibit insufficient manipulability are excluded from the graph, thereby improving computational efficiency and motion robustness.
Fig. 2: Cost-optimal paths and foothold sequences for a multi-limbed intra-vehicular robot on discrete graspable points in the ISS under different computation settings with A ∗ . The thin and thick green lines indicate the explored candidate base paths and the selected base path, respectively. The half-transparent red and blue lines represent the foothold sequences for the corresponding limbs.
CaseAlgorithm SettingsMetrics for the Result Evaluation
Manipulability-basedCost typeExploredTotal baseTotal stepwm,min⁡w_{\mathrm{m},\min} [-]wb,min⁡w_{\mathrm{b},\min} [-]Computation
node selectionnodes [-]translation [m]count [-]time [s]
1nobase translation1724.460.070.00081.2
2yesbase translation3864.570.100.00151.8
3yesstep count404.750.120.00180.2
4yesbase translation725.070.120.00200.2
TABLE I: Quantitative comparison among the simulation result cases.
Fig. 3: Cost-optimal paths and foothold sequences for a multi-limbed intra-vehicular robot on reduced graspable points of Case 4.
Fig. 4: Dynamic simulation of MLIVR locomotion in an ISS module using the proposed path and foothold planner. Straight lines indicate the planned base path (green) and foothold sequence (red and blue), whereas bright red and blue lines represent the actual trajectories tracked by the robot controller. The motion between successive planned stances is interpolated using quintic polynomial trajectories.

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